CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleImage Segmentation Basing on Mathematical Morphology and Region Merging
Thesis Advisor林鹏
Degree Grantor中国科学院自动化研究所
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Abstract本论文利用数学形态学的简化算子、连通算子和水线变换,结合图象的边 界和区域技术给出了一个可适用于多维图象的混合分割算法。 首先利用连通算子滤除噪声、简化图象,保留物体的边缘信息,从而精确 地得到梯度图象;然后基于梯度图象进行水线变换得到图象的初始区域;最后 以这些初始区域作为输入,从下向上地进行区域合并得到最终的分割结果。该 合并过程是一个循环过程,用region adjacency graph(RAG)表示图象的各个 区域极其相邻关系。在每一次合并中,首先抽取最为相似的两个区域,将它们 合并,并更新RAG图中相关的元素。 为了提高该算法的运算速度,我们在图象简化、水线变换和区域合并过程 均采用了分级队列技术,只考虑那些与所需信息相关的象素或区域,这样减少 了图象的扫描次数,大大提高了运算速度。 分割的最终结果可以精确地得到单一象素宽的物体的闭环轮廓,这样可以 抽取小的或细长的特征区域,克服了传统的watershed-plus-markers分割方法的 缺陷。 另外,该算法提出了一个比较通用的合并算法,选择了不同的合并顺序、 合并准则和区域模型,可以实用于不同的目的和场合。它结合了图象分割和滤 波的双重特点,既可以用来分割图象,又可以作为滤波工具。用它构造出新的 连通算子,不仅能同时处理图象中亮的和暗的部分,而且还处理中间过渡区域; 如果我们选取了更为复杂的合并顺序,还可以构造出更好的连通算子,并且这 种连通算子可能突破了传统连通算子认为物体是由图象中亮的或暗的部分组成 的局限。 本文利用了该算法进行了二维图象的简化和分割,并给出了分割结果和性 能分析。
Other AbstractIn this paper, a hybrid multi-dimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of simplification, connected operators and watersheds. At first, in order to compute an accurate estimate of the image gradient, an edge- preserving connected operator is used as a preprocessing stage to simply the image and reduces noise. Then, applying the watershed transform on image gradient magnitude produces an initial partitioning of the image into primitive regions. This initial segmentation is the input to a bottom-up region merging process that produces the final segmentation. The latter process is iterative and uses the region adjacency graph (RAG) representation of the image region and the relationship between regions. At each merging step, the most similar pair of regions is determined, the regions are merged and the RAG is updated. At the steps of image simplification, watershed transform and region merging, a hierarchical queue is used and information is propagated only in the relevant image parts. As a result, the efficiency of the algorithm turns out to be very high. The final segmentation provides one-pixel wide, closed, and accurately localized contours. Thus, we can get small or long-and-thin parts of the image, nor can the traditional watershed-plus-markers method. Furthermore, a general region-merging algorithm is present in this paper. Based on different merging order, merging criterion and region model, many operators can be created. Benefiting from the filtering and segmentation viewpoints, it can be used as both segmentation and filter tool. The new connected operators are not only self- dual, but also can reduce transition regions. If more complex function is used as merging order, new connected operators can be created which have no assumption that objects composing scene are either bright or dark image components. Experimental results and analysis obtained with 2D image are presented in the paper.
Other Identifier508
Document Type学位论文
Recommended Citation
GB/T 7714
李发银. 基于形态学和区域合并的图象分割[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,1999.
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